A preponderance of images collected by photographers contain people, which are often the most important subjects of the images. Knowledge of the presence and location of people in an image, and especially the presence and location of their faces, could enable many beneficial improvements to be made in the image capture process. Some are suggested in the prior art. For example, automatic and semi-automatic focusing cameras often pick a portion of the scene on which to adjust for best focus. If the camera could locate the faces in a scene, then focus could be optimized for the faces unless the photographer explicitly overrides that choice. In U.S. Pat. No. 5,835,616 a face detection system is used in automated photography to eliminate manual adjustment problems that can result in poor quality from lack of focused subjects.
Furthermore, detection of the faces in a scene gives very strong evidence of the proper location of the principal subject matter. In that connection, the process disclosed in the '616 patent automatically finds a human face in a digitized image taken by a digital camera, confirms the existence of the face by examining facial features and then has the camera automatically center itself on the detected face. Detection of a face also yields strong evidence of proper color balance for the facial and/or skin area. For example, in U.S. Pat. No. 5,430,809 a video camera autonomously tracks a facial target in order to set a measuring frame on the facial object for purpose of auto exposure and auto focus. In addition, once the measuring frame is set, an auto white balance system adjusts colors to obtain optimal skin color on the face. As a result, the auto white balance system is said to perform auto skin color balance. It is also known (from U.S. Pat. No. 5,629,752) to detect a human face and then to utilize data representing color and/or density of the facial region to determine an exposure amount such that the region corresponding to the face can be printed appropriately by a photographic printer.
While face detection has been studied over the past several years in relation to the subject of image understanding, it remains an area with impressive computational requirements, particularly if a robust face detection algorithm is needed. A number of methods have been devised that show reasonable performance over a range of imaging conditions. Such methods may be more successfully implemented in large scale processing equipment, such as photographic printers, which have relatively sophisticated processing capability (compared to a hand-held camera). The challenge is to implement these face detection methods reasonably in a camera with limited memory resources, and with low computational cost. If this can be done successfully, the detection of faces in a scene will then serve as a springboard to numerous other improvements in the image capture process. In addition, it would be useful to detect faces in order to implement downstream activities after image capture, e.g., face detection could provide evidence of up/down orientation for subsequent printing (for example, of index prints).